Hydrophobically Associated Hydrogel for High Sensitivity and Resolution of an Interdigital Electrode Pressure Sensor.

Hydrogel-based flexible strain sensors have been known for their excellent ability to convert different motions of humans into electrical signals, thus enabling real-time monitoring of various human health parameters. In this work, a composite hydrogel with hydrophobic association and hybrid cross-linking was fabricated by using polyacrylamide (PAm), surfactant sodium dodecyl sulfate (SDS), lauryl methacrylate (LMA), and polypyrrole (PPy). The dynamic dissociation-conjugation among LMA, SDS, and PPy could dissipate energy to improve the toughness of hydrogels. The SDS/PPy/LMPAm composite hydrogel with a toughness of 1.44 MJ/m3, tensile fracture stress of 345 kPa, tensile strain of 1021%, and electrical conductivity of 0.57 S/m was obtained. Furthermore, an interdigital electrode flexible pressure sensor was designed to replace the bipolar electrode flexible pressure sensor, which greatly improved the sensitivity and resolution of the pressure sensor. The SDS/PPy/LMPAm composite hydrogel-based interdigital electrode flexible pressure sensor showed extraordinary stability and identified different hand gestures as well as monitored the pulse signal of humans. Moreover, the characteristic systolic and diastolic peaks were clearly observed. The pulse frequency (65 times/min) and the radial artery augmentation index (0.57) were calculated, which are very important in evaluating the arterial vessel wall and function of human arteries.

Highly Elastic, Sensitive, Stretchable, and Skin-Inspired Conductive Sodium Alginate/Polyacrylamide/Gallium Composite Hydrogel with Toughness as a Flexible Strain Sensor.

As a classic flexible material, hydrogels show great potential in wearable electronic devices. The application of strain sensors prepared using them in human health monitoring and humanoid robotics is developing rapidly. However, it is still a challenge to fabricate a high-toughness, large-tensile-deformation, strain-sensitive. and human-skin-fit hydrogel with the integration of excellent mechanical properties and high electrical conductivity. In this study, a flexible sensor using a highly strain-sensitive skin-like hydrogel with acrylamide and sodium alginate was designed using liquid metallic gallium as a "reactive" conductive filler. The sensor had a low elastic modulus (30 kPa) similar to that of skin, a high-toughness (2.25 MJ m-3), self-stiffness, a large tensile deformation (1400%), recoverability, and excellent fatigue resistance. Moreover, the addition of gallium might enhance the electrical conductivity (1.9 S m-1) of the hydrogel while maintaining high transparency, and the flexible sensor device constructed from it showed high sensitivity to strain (gauge factor = 4.08) and pressure (gauge factor = 0.455 kPa-1). As a result, the hydrogel sensor could monitor various human motions, including large-scale joint bending and tiny facial expression, breathing, voice recognition, and handwriting. Furthermore, it might even be used for human-computer communication.

Learning to Assess Image Quality Like an Observer.

Human observers are the ultimate receivers and evaluators of the image visual information and have powerful perception ability of visual quality with short-term global perception and long-term regional observation. Thus, it is natural to design an image quality assessment (IQA) computational model to act like an observer for accurately predicting the human perception of image quality. Inspired by this, here, we propose a novel observer-like network (OLN) to perform IQA by jointly considering the global glimpsing information and local scanning information. Specifically, the OLN consists of a global distortion perception (GDP) module and a local distortion observation (LDO) module. The GDP module is designed to mimic the observer's global perception of image quality through performing classification of images' distortion categories and levels. Simultaneously, to simulate the human local observation behavior, the LDO module attempts to gather the long-term regional observation information of the distorted images by continuously tracing the human scanpath in the observer-like scanning manner. By leveraging the bilinear pooling layer to collaborate the short-term global perception with the long-term regional observation, our network precisely predicts the quality scores of distorted images, such as human observers. Comprehensive experiments on the public datasets powerfully demonstrate that the proposed OLN achieves state-of-the-art performance.